Mobile content attribute recommendation engine

ABSTRACT

At least one analytical agent extracts a plurality of attributes from each of a plurality of member input vectors. Each member input vector includes raw data characterizing contextual aspects about an associated and different user. Thereafter, a content search vector is generated for each user by the at least one analytical agent that includes the attributes extracted from the member input vector associated with such user and weights corresponding to each attribute that are particular to such user. A search engine, accessing a content library, then matches each content search vector with one of a plurality of content workflows based on both the attributes and weights within such content search vector. A context engine then initiates execution of each matching content workflow which results in tailored messages specified by the matching content workflow being sent to the user associated with the matching content workflow.

RELATED APPLICATIONS

The current application claims priority to U.S. patent application Ser.No. 15/265,767 filed on Sep. 14, 2016 which, in turn, claims priority toU.S. patent application Ser. No. 62/284,027 filed on Sep. 18, 2015, thecontents of both of which are hereby fully incorporated by reference.

BACKGROUND

Enterprises are increasingly interacting with large number of users viamessaging. As the populations of users continues to increase, so doesthe difficulty in individually engaging such users via messaging toadopt or otherwise drive certain actions/behavior. This situationpresents a problem of scale in two meaningful ways: complexity andvolume. As volume of members increases, it is not possible tocost-effectively support the large population that is presenting vastamounts of data on a continuous basis. The enterprise is not able tohave human representatives that can realistically factor in themultitude of data points that are coming in and the volume of data thatis being generated by these data sources.

SUMMARY

In one aspect, at least one analytical agent extracts a plurality ofattributes from each of a plurality of member input vectors. Each memberinput vector includes raw data characterizing contextual aspects aboutan associated and different user. Thereafter, a content search vector isgenerated for each user by the at least one analytical agent thatincludes the attributes extracted from the member input vectorassociated with such user and weights corresponding to each attributethat are particular to such user. A search engine, accessing a contentlibrary, then matches each content search vector with one of a pluralityof content workflows based on both the attributes and weights withinsuch content search vector. A context engine then initiates execution ofeach matching content workflow which results in tailored messagesspecified by the matching content workflow being sent to the userassociated with the matching content workflow.

In some variations, the member input vector can be generated for eachusers. The attributes of the member input vector can be subject tochange such that the extracting, generating, matching, and initiatingare updated/continually updated to reflect changes in the member inputvector. A dimensionality of the member input vector is automaticallyexpanded upon addition of one or more data sources without refactoringother data sources.

A plurality of responses can be received for each user that areresponses to tailored messages previously sent to such user. In suchcases, computer-implemented natural language processing on the pluralityof responses to generate at least a portion of the attributes of eachmember input vector.

Each content workflow can be stored within a database and they canspecify a sequence, modality, and delivery flow of the tailoredmessages. The modality can include at least one of: short messagingservice, multimedia messaging service, application notification, ore-mail message.

There can be a plurality of analytical agents such that at least oneanalytical agent uses an output of at least one other analytical agentin connection with the extracting and generating. In addition, there canbe a plurality of analytical agents that each evaluate only a subset ofdimensions of the member input vector and which generate only adifferent subset of the attributes.

The analytical agents can take many forms. For example, the at least oneanalytical agent can include a natural language processing agent toextract key topics from a user-generated response and which uses amachine learning model. The at least one analytical agent can include amapping agent that runs data mapping rules to map data falling within arange into attribute. The at least one analytical agent can include aclassification agent using random forests to classify continuous featurevectors with a finite set of classes. The at least one analytical agentcan include an emotion recognition agent that takes individual messagesand generates an emotional profile of the messages. The at least oneanalytical agent can include a psychographic monitoring agent thattranslates user generated self-reports or provided outcomes obtainedfrom external sources to generate a psychographic profile for a user.

In some implementations, a forward index is generated to store a listcorresponding to all of the attributes. This forward index can beinverted to result in an inverted index that is used by the searchengine to match attributes of the content search vector with attributesof the content workflows.

An activation score can be generated for each attribute in each contentsearch vector. This activation score can be used by the search engine toidentify a best matching content workflow.

The tailored messages can pertain to a wide variety of scenariosincluding a healthcare and/or wellness regimen.

The activation score can be based on a variety of factors. For example,the activation score can be based on a self efficacy score derived fromresponses by the respective user of self-efficacy assessments. Theactivation score can be based on a state of change of the respectiveuser. The activation score can be based on a current internal change ofthe respective user that characterizes actions performed by therespective user in relation to overall behavior change goals. Theactivation score can be based on a current behavior change of therespective user that characterizes actions performed by the respectiveuser in relation to overall behavior change goals. The activation scorecan be based on a current engagement level of the respective usercharacterizing how the user responds to the tailored messages.

It can be determined, for each content search vector, a distance betweenthe content search vector and each of a plurality of content workflows.This distance can be used by the search engine to match a contentworkflow having a shortest determined distance relative to thecorresponding content search vector.

Each content workflow can include a plurality of messages that are eachtagged with respective attributes characterizing content of suchmessages. Each content workflow can include or otherwise specify acontent matching algorithm that uses a content search vector that hasweights that are determined by a genetic algorithm such that a fitnessmeasure for optimization is the resulting activation score.

Non-transitory computer program products (i.e., physically embodiedcomputer program products) are also described that store instructions,which when executed by one or more data processors of one or morecomputing systems, cause at least one data processor to performoperations herein. Similarly, computer systems are also described thatmay include one or more data processors and memory coupled to the one ormore data processors. The memory may temporarily or permanently storeinstructions that cause at least one processor to perform one or more ofthe operations described herein. In addition, methods can be implementedby one or more data processors either within a single computing systemor distributed among two or more computing systems. Such computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g., the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The subject matter described herein provides many technical advantages.For example, the methods, systems, and computer program productsprovided herein enable dynamic, tailored and large-scale contentcreation and delivery that maximizes content relevance and increasesuser activation among a large population. In addition, the currentsubject matter, in contrast to conventional messaging systems, providestechnical advantages by automatically recommending and selecting themost individualized and relevant content in real-time, which increasesengagement and promotes positive behavior change among a largepopulation of users.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating a computing architecture fordistributing tailored messages to a large user population.

FIG. 2 is a diagram illustrating a system for determining tailoredcontent for inclusion in messages distributed to a large userpopulation;

FIG. 3 is a diagram illustrating a sentiment analyzer for generating asentiment score;

FIG. 4 is a process flow diagram for providing a stage of changeestimator that can determine to which of the six Stages of Change amember belongs;

FIG. 5 is a diagram illustrating a behavior analyzer for generating abehavioral performance score;

FIG. 6 is a process flow diagram illustrating generation of tailoredmessages to a large population of users; and

FIG. 7 is a diagram illustrating a computing device for implementingvarious aspects of the subject matter described herein.

DETAILED DESCRIPTION

With reference to diagram 100 of FIG. 1, the current subject matter isdirected to an advanced computing platform including a messagegeneration and delivery system 130 that allows for tailored messages tobe sent to and exchanged with a large user population via, for example,various computing device 120 such as mobile phones communicating withthe message generation and delivery system 130 via one or morewired/wireless computing networks. In particular, the message generationand delivery system 130 can allow for the combination and visualizationof qualitative and quantitative data from disparate sources and systemsand allows for a machine learning model to be trained to send tailoredmessages using a context engine.

The current subject matter can be applied to a wide variety of scenariosin which there is a need to send tailored messages to a large populationof members. Examples include: a) a health plan that needs to changemember behavior to use lower cost resources (such as Urgent Care or anhealth advice line) instead of high-cost resources (such as EmergencyRoom), b) a medical provider that needs to ensure that its patients areadhering to the medication regimen suggested by the doctor; c) aprovider that wants to ensure that patient appointments are not missed,or d) a pharmacy system that needs to ensure that its patients refillthe prescription on time. Another example for both healthcare contextand non-healthcare includes an organization that wants to changemembers' preferences of getting paper-based mail regarding theirbenefits or account statements to receiving these communicationsdigitally where members receive these statements in a digital format.For illustration purposes only, references are made herein predominantlyto the use of the computing platform in connection with healthcareapplications.

Many health plan members, consumers and patients (referred tointerchangeably as “members,” “consumers,” or “patients”) seekencouragement, reminders, education, health tips and information andwould like to actively engage with mobile solutions to build healthyhabits and behaviors. Others are more resistant to change but can stillbe nudged to take small steps to improve their health with the rightsupport. At the same time, health plans and providers are eager to addinnovative mobile health solutions to their existing networks, to reachtheir patients to track their progress, to provide them with ongoingcare, to understand their needs, and to help drive behavior change. Thecurrent subject matter leverages behavioral science research to buildnovel, interactive and intelligent workflows that are tuned to specificconditions (such as diabetes, periodontal disease, asthma, smoking) anduses analytics and algorithms to provide targeted content that matchesthe needs of consumers and patients based on their specificcharacteristics and individual data profiles.

The current subject matter relates to an on-going and in-depth analysisof member characteristics and behavior patterns to optimize therelevance of health message content and improve engagement. It canrecommend content workflows and can use an algorithm that relies onnumerous (e.g., over 100, etc.) attributes to recommend the best contentfor a specific user at a particular time. The current subject matteruses intelligent algorithms that can map point-in-time data into asearch vector that is used to select the best next workflow. Thealgorithms can be iterative where each successive iteration attempts toincrease the member's activation score.

Furthermore, the current subject matter can use natural languageprocessing (NLP) to analyze member responses, and then combines NLPtechniques to engage in a more human-centered dialog. NLP, in thisregard, can be used to understand unexpected responses that might havethe same semantic value as an expected/structured response. In contrastto messaging platforms that use fixed and strict pattern matching rules,the current subject matter allows message content to be dynamicallychanged if member preferences and behavior fall outside these structuredrules.

FIG. 2 is a diagram 200 illustrating aspects of the current computingplatform (sometimes referred to herein as the cognitive computingarchitecture) which can form part of the message generation and deliverysystem 130 (which in turn can form a plurality of distinct softwaremodules and/or computing systems that can be distributed or form part ofa single integrated computer system). The cognitive computingarchitecture can use individual data processing agents that processincoming stimuli (new data) into a knowledge representation forinformation retrieval (a content search vector). A member input vector210 can be a dynamically expanding input vector about a member at agiven point-in-time comprising several elements. The member input vector210 in one example, can be broken into three primary categories:psychographic information 212, demographic information 214, andbehavioral information 216. The member input vector 210 can expand indimensionality when new data sources are made available. Thisarrangement provides a significant technical advantage over existingplatforms in that current platforms assume a fixed input and cannoteasily incorporate new data dimensions or sources without significantrefactoring. In the subject matter described herein, the architecture isable to dynamically incorporate new input dimensions without refactoringexisting input sources. The basis for this capability and advantage isdescribed below.

The member input vector 210 can be processed by analytical agents 220which are a set of processes whose outputs are combined to generate acontent search vector 230. An analytical processing agent 220 can bebelong to one of the following types: natural language agents, mappingagents, classification agents, emotion recognition agents, psychographicmonitoring agents, and communication agents. These agents will bedescribed in more detail below.

The content search vector 230 can comprise a set of search attributesand their weights. A search vector attribute can be made up of one ormore words and is similar to a search term used within a search engine.Each attribute in the content search vector 230 can have a weight thatdetermines the importance of that search attribute within the search.

The content workflow search engine 240 can be an information retrievalsystem that retrieves the best content workflow by maximizing the matchbetween content workflows stored within a content library 250 and thecontent search vector 230.

The content library 250 (sometimes referred to as a content workflowlibrary) is a database of content workflows. Each content workflowwithin the database consists of one or more messages to be delivered tousers and rules that dictate the sequence, modality, and flow of thesemessages. Each workflow also has an indexed list of attributes that areused to match against in the information retrieval task.

The context engine 270 is a complex rules engine that is built to manageand execute a content workflow 260 selected by the search engine 240. Akey performance indicator generated by the context engine 270 can be anactivation score of the member (as described further below). This scorecan be calculated by factoring self-efficacy, stage of change, internalchange, engagement and behavior change (specifically goal completion).

Input dimensions to the member input vector 210 can comprise numerical(including, date/time), nominal or textual values. The number ofdimensions and the sparseness of the dimensions can change over time andthe current subject matter is architected to support such an evolvingdimensionality. An example set of dimensions is provided below in Table1.

TABLE 1 Demographic Psychographic Behavioral Date of birth Self-careText-Ins - Informational Gender Social support Text-Ins - Challenge ZipSelf-efficacy Time to respond Language Readiness to change Length ofresponse Health literacy level Motivation Sentiment of responseCommunity need index Confidence Frequency of response Marital statusHealth beliefs Tone/style of response Living alone Stage of changeEngagement Children in household Stress levels Last Visit Date Healthrisks Activation Last Visit code Secondary risk factors Depression NextAppointment Date Health status Assessment Next Visit Code Exerciselevels Lifestyle Nutrition habits Values Weight issues Smoker

The analytical agents 220 can be characterized as semi-intelligentagents that process the member input vector 210 to generate the mostappropriate search attributes and their corresponding weights. The agentmodel can be layered in that some agents use the output of other agentsto compute their outputs. Such an agent architecture allows forindependent processing at scale and supports the easy addition of newinput dimensions and attribute generators. Each agent is uniquelydeveloped to evaluate and process only certain dimensions of the inputvector and output one or more attributes.

While the calculation is unique, the current subject matter provides forsix main types of agents. Furthermore, other types of agents can beutilized in different combinations and interrelationships.

Natural language processing (NLP) agents can be used to extract keytopics from a user response using a machine learning model such assupport vector machines (SVM). Human categorization would be tootime-consuming, costly, and inconsistent, requiring a semi-automaticsystem of text categorization. The goal of text categorization is theclassification of documents into a fixed number of predefinedcategories. SVM is a supervised learning algorithm that can be used todetermine whether the response from the member belongs to multiplecategories, a single category, or no category. Linear kernels can beused for the SVM to ensure increased accuracy with small training sets.Most text classification tasks are linearly separable and linear kernelsare particularly well suited for text-categorization. Simple linear SVMsalso provide good generalization accuracy and are faster to train.

Mapping agents can be agents that run data mapping rules that map datathat falls within a range of data values into attributes using databoundary rules. An example of this would be an age mapping agent thatmaps a member's year of birth (DOB) into attributes based several rules:

IF (CY - 18) > year(DOB)) AND (CY - 13) < year(DOB)) THENADD_ATTRIBUTE(“teen”) IF (CY - 30) > year(DOB)) AND (CY - 18) <year(DOB)) THEN ADD_ATTRIBUTE(“young adult”) ... CY - Current Yearyear(dob) - Returns the year given a timestampadd_attribute(search_term) - Function that adds the search_term to thesearch vector

Classification agents can use random forests, a classification algorithmthat classifies continuous feature vectors (such as time to respond,length of response, sentiment of response) with a finite set of classes(‘slow to respond’, ‘respond well’). These classification agents cantake values from other computed values (such as sentiment) and use themin their calculation. Random forests, in this context, can use anensemble learning approach and can be used as a powerful predictivealgorithm because they average multiple decision trees and thus avoidbias and overfitting. An alternative to using random forest would be touse a Naïve Bayes classifier, especially for smaller models and to getan overview of the data before running the more complex random forestalgorithm.

Emotion recognition agents can take individual messages (e.g., textmessages, etc.) and generate an emotional profile of the message forfurther processing. These emotion recognition agents can includesentiment and tone analyzers (using a lexicographic approach) along withgeneral emotion analysis of a member's response.

Psychographic monitoring agents can translate self-report or providedoutcomes data from external sources to determine a member'spsychographic profile. These agents are interested in the following datasources and related attributes such as, for example, self-care, socialsupport, self-efficacy, readiness to change, motivation, confidence,health beliefs, stage of change, stress levels, activation, lifestyle,and values. The algorithms used by the psychographic monitoring agentscan be rule-based and driven by evidence-based protocols and generallyaccepted models within cognitive and behavioral psychology research suchas behavioral science (e.g., stages of change, goal setting, gametheory, prospect theory, behavioral economics, adherence models, healthbelief model, self-efficacy, etc.), evidence-based clinical protocols(e.g., diabetes management protocols, smoking cessation programs,periodontal disease management, etc.) and population health management(e.g., patient activation model, wellness programs, etc.).

FIG. 3 is a logic diagram 300 illustrating an example psychographicmonitoring agent that can provide a sentiment score based on text miningand sentiment analysis of words and phrases in message responsesreceived by the message generation and delivery system 130 from thecomputing devices 120 over the network 110. The purpose of the sentimentanalyzer can be to extract a sentiment score for each response in orderto better understand how people are reacting to these messages (e.g.,messages sent by SMS or MMS, etc.) by studying the polarity or valenceof their responses. The sentiment score can be compiled using a hybridtwo-step approach that combines matching algorithms with classificationalgorithms. In step one, an unsupervised matching model can extractterms used in text messages and matches them to a health care specificlexicon of known words and phrases. The lexicon-based approach involvescalculating orientation for a document from the semantic orientation ofwords or phrases in the document.

In step two, a supervised classification model can use training data torecognize and predict polarity for unseen terms drawn from a largeannotated corpus. Together, these two models can capture the tone ofmember responses as well as the emotional trajectory on an individualmember level, on an aggregate member level, for a particular health orwellness campaign, and over time. The text classification approach canuse statistical or machine-learning (SVM) models and involves buildingclassifiers from labeled instances of texts or sentences. Theclassifiers can be trained on data sets using unigrams or bigrams

FIG. 4 is a flow diagram 400 illustrating an example of anothermonitoring agent that can be used to determine a member's stage ofchange. As part of the current subject matter, this algorithm can beused to assess a member's “stage of change” using a transtheoreticalchange model (Prochaska & DiClemente, 1983). The stages of changeinclude Precontemplation, Contemplation, Preparation, Action,Maintenance, and Relapse. Stage of change is assessed through a refinedand shorter version of the Processes of Change Questionnaire using afive-point Likert format. Assessment dialog workflows (a form of contentworkflow that is focused on assessment) can be developed and used toassess a member's stage of change. The data from these assessments canbe used in the process described within the flow diagram 400 todetermine the member's Stage of Change (SOC). For a new member, the SOCis not yet known and the member is assigned the Precontemplation SOC410, which can result in an outgoing message being sent 420 that elicitsa response. This message can be a regular campaign message and themember is expected to respond (MO) 460 and the response can be processedfor SOC indicators within the MO analysis process 470 and, based on thisanalysis, a new SOC 480 can be assigned. Within a predefined period oftime (e.g., 30 days, etc.) of non-response, messages can be sent to themember that comprise an SOC Poll (a specific Stage of Change assessment)490 to explicitly determine the member's SOC. However, if the memberdoes not respond 430, after 30 days, the member can be automaticallyassigned or kept on the Precontemplation stage.

FIG. 5 is a block diagram 500 illustrating an example of anotherpsychographic monitoring agent that provides a behavioral performancescore. A pattern analysis engine can perform mining (e.g., daily mining,etc.) on behavior variables such as mean time to respond, response ratioand time/day of response. The pattern analysis engine can identifyfrequent and recurring patterns and uses a “bag of patterns” (BoP)approach and feature selection techniques that identifies relationshipsbetween behavior pattern vectors and outcomes. The pattern mining willalso be combined with visualization to show how certain behaviorpatterns are more closely associated with particular outcomes.

Communication agents are types of analytics agents that can be focusedon the channel or communication and compliance associated with thatchannel. For example, the agents used to map data to the search vectorattributes might address communication requirements such as TCPAcompliance, language preferences, channel preference, frequencycontrols.

The content search vector 230 can be a combination of the responses fromthe analytical agents 220 that processed the member input vector 210 andgenerated several attributes. The content search vector 230 can compriseattributes (a_(i)) and associated attribute score (S(a_(i))). Theattribute score for a given attribute can be as follows:S(a _(i))=w(a _(i))c(a _(i))where w(a_(i)) is a_(i)'s weight determined by a weighting modeldescribed below. And c(a_(i)) is the attribute's confidence generated bythe analytical agent(s) 220.

The weighting for the weights within the content search vector model canbe determined by a genetic algorithm in which the activation score canbe used as the fitness measure. The goal is to select the contentworkflows that maximize (or increase) the value of the activation score.This consists of running multiple trials on the training set using anevaluation function and technique called “leaving-one-out” in which oneexemplar at a time is withheld as a form of cross-validation. Thisspeeds up the optimization process because many of the feature weights(where covariances are too small or too large) can be eliminated. Thisgenetic algorithm evaluation function (sum of squares of errors on thetraining set) is also commonly used with backpropagation in neuralnetworks. Updates to the weighting using the proposed genetic algorithmare performed infrequently—e.g., it is triggered every 3 months or whenthere is a significant negative shift in the population's activationscore.

The selection process comprises taking a version of a proposed weightingalgorithm and simulating the resulting fitness or activation score. Thefitness measure can be computed by extrapolating activation from priorusage of content workflows and the resultant activation score.

Attribute Weight Confidence Attribute Score 59 year 5 5 25 55-65 yearolds 5 5 25 Baby boomer 5 4 20 Low self-efficacy 3 1 3 SOC:Precontemplation 4 3 12 Low Activation 3 2 6 Medium engagement 2 3 6Upset 1 5 5 Very Upset 1 4 4

The content workflow search engine 240 can use the content search vector230 to match the best content that optimally matches the searchattributes with the attributes of the content workflows. Initially, aforward index can be developed to store the list of attributes and thenit can be inverted to develop an inverted index. This arrangementreduces the time, memory and processing resources needed to perform aquery and optimizes the process. A search engine indexing algorithm canbe used which, in turn, can optimize the speed of the query. Attributescores can be used to rank the resulting documents based on tags matchedand the weights assigned to those tags.

In choosing the best content workflow for a particular member, analgorithm can match the content attributes of each message in theworkflow (e.g., supportive, low health literacy, appropriate for allages, targeting diabetics, nutrition-related, informational, no call toaction) to the member attribute score (e.g., low-income, diabetic, lowmotivation to change, low confidence levels, interested in nutritiontips) to optimize the closest fit and drive engagement with the messagecontent.

The model will always select at least one workflow that is a match for aparticular member based on the highest matching attribute score. Foreach workflow and its corresponding content search vector, a distancecalculation between the workflow and the member's content search vectorcan be used to select the best match workflow. The distance measure canbe defined by the equation below:

${d(c)} = {{\sum\limits_{n = 1}^{N}{w_{n}t_{n}\mspace{14mu}{where}\mspace{14mu} t_{n}}} = \left\{ \begin{matrix}{{- 1}\mspace{14mu}{if}\mspace{14mu} s_{n}\;\;\left\{ c \right\}} \\{{1\mspace{14mu}{if}\mspace{14mu} s_{n}} \in \left\{ c \right\}}\end{matrix} \right.}$where d(c) is the distance for the content workflow c is the set ofsearch terms, w_(n) is the content search vector weight for thatattribute, s_(n).

With each new piece of information (triggered event or scheduled eventfrom the member), the member's content search vector can be re-computedand a new content workflow may be generated. Assignment of a new contentworkflow is determined by two factors: whether an existing workflow iscomplete, or the new suggested workflow is significantly better matchedthan the current workflow.

The content library 250 can contain a large content workflow librarythat can be used as the content source for all messages. Each message inthe workflow library can be tagged with the attributes of the content.Messages and attributes associated with each message can be continuouslyupdated as new content is generated. Cross-validation of attributes fora workflow can be done through various mechanisms includingcrowd-sourcing. Content workflows can be a combination of pre-existingopen source or public domain content such as the National CancerInstitute's “smokefreeTXT” content or proprietary content libraries fromclients and other sources.

The context engine 270 can be a computer program that executes a contentworkflow. This content workflow or “dialog” is similar to a finite statemachine with states, actions and rules. Rules (including NLP-based,string matching-based rules, multi-predicate) determine which actions toperform in a given state. An action may include moving to a new state orsending a message to the member. States represent the context that amember has gone through (and is in) and is a way to ensure that complexdialogs and conversations are compelling and relevant to the member'scurrent mental space.

Behavior pattern algorithms can be combined with the three components ofthe member input vector 210 (psychographic information 212, demographicinformation 214, and behavioral information 216) to generate a holisticand reliable activation metric. This metric can range, for example, from1 to 100 where 100 is the highest level of activation a member canobtain. While this measure is an estimate, that is, it only computes avalue of activation based on known data points, the member activationscore can provide a uniform way to measure and track engagement andactivation of individual members, campaigns, clients and system wide. Insome implementations, an objective can be to increase this score for allmembers by presenting them with the most relevant and timely contentworkflows. This selection process is supported by adaptive learning andanalytical models described above.

The member activation score can be provided as follows:ActivationScore=w _(se) SE+w _(soc) SOC+w _(ic) IC+w _(bc) BC+w _(e) Ewhere: w_(se)SE—is a weighted scoring of the member's current SelfEfficacy (SE) score. The SE score can be derived from responses toself-efficacy assessments that are completed by the member atrecommended intervals. The SE score can also incorporate health beliefprofile data (i.e., self-reported perceptions of the severity of ahealth threat or illness, of susceptibility to that illness, of thebenefits of taking preventive action, and of barriers to taking thataction, etc.). The health belief model addresses the relationshipbetween a person's beliefs and their behavior and predicts how likelyindividuals are to engage in healthy behaviors. The SE score can benormalized to a range of 1-10 (higher scores reflect higherself-efficacy).

w_(soc)SOC is a weighted scoring of the member's current Stage ofChange. The Stage of Change can be based on Prochaska's TranstheoreticalModel of Change which has 6 major stages of change. Each stage maps to ascore from Stages of Change behavior. The SOC score can be derived fromresponses to assessments that determine a member's most likely stage ofchange. The SOC score is normalized to a range of 1-10 (higher scoresrepresent a greater readiness to change).

w_(ic)IC is a weighted scoring of the member's current Internal Change.This score can be derived from actions performed by the member towardsthe related behavioral goals aligned to the specific overall behaviorchange goals. For example, the Behavior Change (BC) goals or outcomevariables for a diabetic may be to improve glycemic control ormedication adherence, but related self-care behavioral goals or internalchanges (IC) might incorporate diet and/or exercise. The score can bethe cumulative value of individual positive behaviors performed by themember. The IC score can be normalized to a range of 1-10 (higher scoresreflect the completion of more positive behaviors).

w_(bc)BC is a weighted scoring of the member's current Behavior Change.This score can be derived from actions performed by the member towardsthe main behavior change goal (determined by the desired healthoutcome). The BC score can be normalized to a range of 1-10 (higherscores reflect greater completion of the behavioral goal).

w_(e)E is a weighted scoring of the member's current Engagement level.This score can be based on how the consumer communicates and interactswith the mobile platform and can be compiled from response data(frequency of response, length of response, time of response, sentimentof response, text-ins for information, text-ins for challenges, etc.).The E score can be normalized to a range of 1-10 (higher scores reflectgreater engagement).

FIG. 6 is a process flow diagram 600 illustrating the generation anddelivery of tailored messages. Initially, at 610, at least one analyticagent extracts a plurality of attributes from each of a plurality ofmember input vectors. Each member input vector includes raw datacharacterizing contextual aspects about an associated and differentuser. Thereafter, at 620, a content search vector is generated for eachuser using the attributes extracted from the member input vectorassociated with such user and weights for each such attribute which areparticular to the user. Next, at 630, a search engine matches eachcontent search vector with a content workflow by accessing a contentlibrary and by using the weights. Next, at 640, a context engineinitiates each content workflow which results in tailored messagesspecified by the content workflow being sent to the user associated withthe content workflow.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code, caninclude machine instructions for a programmable processor, and/or can beimplemented in a high-level procedural language, an object-orientedprogramming language, a functional programming language, a logicalprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable data processor. The machine-readable medium canstore such machine instructions non-transitorily, such as for example aswould a non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

The computer components, software modules, functions, data stores anddata structures described herein can be connected directly or indirectlyto each other in order to allow the flow of data needed for theiroperations. It is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, andcan be implemented for example as a subroutine unit of code, or as asoftware function unit of code, or as an object (as in anobject-oriented paradigm), or as an applet, or in a computer scriptlanguage, or as another type of computer code. The software componentsand/or functionality may be located on a single computer or distributedacross multiple computers depending upon the situation at hand.

FIG. 7 is a diagram illustrating a sample computing device architecturefor implementing various aspects described herein. A bus 704 can serveas the information highway interconnecting the other illustratedcomponents of the hardware. A processing system 708 labeled CPU (centralprocessing unit) (e.g., one or more computer processors/data processorsat a given computer or at multiple computers), can perform calculationsand logic operations required to execute a program. A non-transitoryprocessor-readable storage medium, such as read only memory (ROM) 712and random access memory (RAM) 714, can be in communication with theprocessing system 708 and may include one or more programminginstructions for the operations specified here. Optionally, programinstructions may be stored on a non-transitory computer-readable storagemedium such as a magnetic disk, optical disk, recordable memory device,flash memory, or other physical storage medium.

In one example, a disk controller 748 can interface one or more optionaldisk drives to the system bus 704. These disk drives may be external orinternal floppy disk drives such as 760, external or internal CD-ROM,CD-R, CD-RW or DVD, or solid state drives such as 752, or external orinternal hard drives 756. As indicated previously, these various diskdrives 752, 756, 760 and disk controllers are optional devices. Thesystem bus 704 can also include at least one communication port 720 toallow for communication with external devices either physicallyconnected to the computing system or available externally through awired or wireless network. In some cases, the communication port 720includes or otherwise comprises a network interface.

To provide for interaction with a user, the subject matter describedherein can be implemented on a computing device having a display device740 (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display)monitor) for displaying information obtained from the bus 704 to theuser and an input device 732 such as keyboard and/or a pointing device(e.g., a mouse or a trackball) and/or a touchscreen by which the usermay provide input to the computer. Other kinds of devices may be used toprovide for interaction with a user as well; for example, feedbackprovided to the user may be any form of sensory feedback (e.g., visualfeedback, auditory feedback by way of a microphone 736, or tactilefeedback); and input from the user may be received in any form,including acoustic, speech, or tactile input. In the input device 732and the microphone 736 can be coupled to and convey information via thebus 704 by way of an input device interface 728. Other computingdevices, such as dedicated servers, can omit one or more of the display740 and display interface 724, the input device 732, the microphone 736,and input device interface 728.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such a phrase isintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” In addition, use of the term “based on,” aboveand in the claims is intended to mean, “based at least in part on,” suchthat an unrecited feature or element is also permissible.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A computer-implemented method comprising:extracting, by at least one analytical agent for each of a plurality ofdifferent users, a plurality of attributes from each of a plurality ofmember input vectors, each member input vector comprising raw datacharacterizing contextual aspects about such user; generating, for eachuser by the at least one analytical agent, a content search vectorcomprising the attributes extracted from the member input vectorassociated with such user and weights corresponding to each attributethat are particular to such user; matching, by a search engine accessinga content library, each content search vector with one of a plurality ofcontent workflows based on both the attributes and weights within suchcontent search vector; and initiating, by a context engine, execution ofeach matching content workflow which results in tailored messagesspecified by the matching content workflow being sent to the user by atleast one delivery modality associated with the matching contentworkflow, the tailored messages being directed to health plan members tobuild healthy habits and behaviors.
 2. The method of claim 1, furthercomprising generating the member input vector for each user.
 3. Themethod of claim 2, wherein attributes of the member input vector aresubject to change and the extracting, generating, matching, andinitiating are updated to reflect changes in the member input vector. 4.The method of claim 3, wherein a dimensionality of the member inputvector is automatically expanded upon addition of one or more datasources without refactoring other data sources.
 5. The method of claim1, further comprising: receiving, for each user, a plurality ofresponses to tailored messages previously sent to such user; performingcomputer-implemented natural language processing on the plurality ofresponses to generate at least a portion of the attributes of eachmember input vector.
 6. The method of claim 1, wherein each contentworkflow is stored within a database and specifies a sequence, modality,and delivery flow of the tailored messages.
 7. The method of claim 6,wherein the modality comprises at least one of: short messaging service,multimedia messaging service, application notification, or e-mailmessage.
 8. The method of claim 1, wherein there are a plurality ofanalytical agents and at least one analytical agent uses an output of atleast one other analytical agent in connection with the extracting andgenerating.
 9. The method of claim 1, wherein there are a plurality ofanalytical agents that each evaluate only a subset of dimensions of themember input vector and which generate only a different subset of theattributes.
 10. The method of claim 1, wherein the at least oneanalytical agent comprises a natural language processing agent toextract key topics from a user-generated response and which uses amachine learning model.
 11. The method of claim 1, wherein the at leastone analytical agent comprises a mapping agent that runs data mappingrules to map data falling within a range into attribute.
 12. The methodof claim 1, wherein the at least one analytical agent comprises aclassification agent using random forests to classify continuous featurevectors with a finite set of classes.
 13. The method of claim 1, whereinthe at least one analytical agent comprises an emotion recognition agentthat takes individual messages and generates an emotional profile of themessages.
 14. The method of claim 1, wherein the at least one analyticalagent comprises a psychographic monitoring agent that translates usergenerated self-reports or provided outcomes obtained from externalsources to generate a psychographic profile for a user.
 15. The methodof claim 1 further comprising: generating a forward index to store alist corresponding to all of the attributes; inverting the forward indexto result in an inverted index, the inverted index being used by thesearch engine to match attributes of the content search vector withattributes of the content workflows.
 16. The method of claim 1 furthercomprising: generating an activation score for each attribute in eachcontent search vector; wherein the matching by the search engineutilizes the generated activation scores to identify a best matchingcontent workflow.
 17. The method of claim 16, wherein the tailoredmessages pertain to a healthcare and/or wellness regimen.
 18. The methodof claim 17, wherein the activation score is based on a self efficacyscore derived from responses by the respective user of self-efficacyassessments.
 19. The method of claim 17, wherein the activation score isbased on a state of change of the respective user.
 20. The method ofclaim 17, wherein the activation score is based on a current internalchange of the respective user that characterizes actions performed bythe respective user in relation to overall behavior change goals. 21.The method of claim 17, wherein the activation score is based on acurrent behavior change of the respective user that characterizesactions performed by the respective user in relation to overall behaviorchange goals.
 22. The method of claim 17, wherein the activation scoreis based on a current engagement level of the respective usercharacterizing how the user responds to the tailored messages.
 23. Themethod of claim 1 further comprising: determining, for each contentsearch vector, a distance between the content search vector and each ofa plurality of content workflows; wherein the matching by the searchengine is based on the content workflow having a shortest determineddistance relative to the corresponding content search vector.
 24. Themethod of claim 1, wherein each content workflow comprises a pluralityof messages that are each tagged with respective attributescharacterizing content of such messages.
 25. The method of claim 16,wherein each content workflow comprises a content matching algorithmthat uses a content search vector that has weights that are determinedby a genetic algorithm wherein a fitness measure for optimization is theresulting activation score.
 26. A computer-implemented method forincreasing compliance with a healthcare/wellness regimen, the methodcomprising: extracting, for each of a plurality of different users, aplurality of attributes from each of a plurality of member inputvectors, each member input vector comprising raw data characterizingcontextual aspects about such user; generating, for each user, a contentsearch vector comprising the attributes extracted from the member inputvector associated with such user and weights corresponding to eachattribute that are particular to such user; matching, by accessing acontent library, each content search vector with one of a plurality ofcontent workflows based on both the attributes and weights within suchcontent search vector, the content workflows relating to compliance withthe healthcare/wellness regiment; and initiating execution of eachmatching content workflow which results in tailored messages specifiedby the matching content workflow being sent to the user by at least onedelivery modality associated with the matching content workflow, thetailored messages being directed increasing compliance with thehealthcare/wellness regimen.
 27. A computer-implemented methodcomprising: extracting, for a user, a plurality of attributes from eachof a plurality of member input vectors, each member input vectorcomprising raw data characterizing contextual aspects about thedifferent user; generating, for the user, a content search vectorcomprising the attributes extracted from the member input vectorassociated with the user and weights corresponding to each attributethat are particular to the user; matching, by a search engine accessinga content library, each content search vector with one of a plurality ofcontent workflows based on both the attributes and weights within suchcontent search vector; and initiating, by a context engine, execution ofeach matching content workflow which results in tailored messagesspecified by the matching content workflow being sent to the user, thematching content workflows each specifying a different sequence,modality, and delivery flow of the tailored messages.